

from typing import List, Optional, Tuple, Union

import torch

from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline


__a    :Union[str, Any]      =			logging.get_logger(__name__)  # pylint: disable=invalid-name

class   _a					(			snake_case_				):




      """simple docstring"""

      def __init__(   self							:				Optional[int]						,			UpperCAmelCase							:				int						,			UpperCAmelCase							:				Any	):
            super().__init__()
            self.register_modules(unet=UpperCAmelCase						,			scheduler=UpperCAmelCase	)







      @torch.no_grad()
      def __call__(   self							:				Dict						,			UpperCAmelCase							:				int = 1						,			UpperCAmelCase							:				int = 100						,			UpperCAmelCase							:				Optional[Union[torch.Generator, List[torch.Generator]]] = None						,			UpperCAmelCase							:				Optional[float] = None						,			UpperCAmelCase							:				bool = True						,			):

            if audio_length_in_s is None:
                  A_						  =				self.unet.config.sample_size / self.unet.config.sample_rate

            A_						  =				audio_length_in_s * self.unet.config.sample_rate

            A_						  =				2 ** len(self.unet.up_blocks	)
            if sample_size < 3 * down_scale_factor:
                  raise ValueError(
                      f'''{audio_length_in_s} is too small. Make sure it\'s bigger or equal to'''
                      f''' {3 * down_scale_factor / self.unet.config.sample_rate}.'''	)

            A_						  =				int(UpperCAmelCase	)
            if sample_size % down_scale_factor != 0:
                  A_						  =				(
                      (audio_length_in_s * self.unet.config.sample_rate) // down_scale_factor + 1
                  ) * down_scale_factor
                  logger.info(
                      f'''{audio_length_in_s} is increased to {sample_size / self.unet.config.sample_rate} so that it can be handled'''
                      f''' by the model. It will be cut to {original_sample_size / self.unet.config.sample_rate} after the denoising'''
                      " process."	)
            A_						  =				int(UpperCAmelCase	)

            A_						  =				next(iter(self.unet.parameters()	)	).dtype
            A_						  =				(batch_size, self.unet.config.in_channels, sample_size)
            if isinstance(UpperCAmelCase						,			UpperCAmelCase	) and len(UpperCAmelCase	) != batch_size:
                  raise ValueError(
                      f'''You have passed a list of generators of length {len(UpperCAmelCase	)}, but requested an effective batch'''
                      f''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''	)

            A_						  =				randn_tensor(UpperCAmelCase						,			generator=UpperCAmelCase						,			device=self.device						,			dtype=UpperCAmelCase	)

            # set step values
            self.scheduler.set_timesteps(UpperCAmelCase						,			device=audio.device	)
            A_						  =				self.scheduler.timesteps.to(UpperCAmelCase	)

            for t in self.progress_bar(self.scheduler.timesteps	):
                  # 1. predict noise model_output
                  A_						  =				self.unet(UpperCAmelCase						,			UpperCAmelCase	).sample

                  # 2. compute previous image: x_t -> t_t-1
                  A_						  =				self.scheduler.step(UpperCAmelCase						,			UpperCAmelCase						,			UpperCAmelCase	).prev_sample

            A_						  =				audio.clamp(-1						,			1	).float().cpu().numpy()

            A_						  =				audio[:, :, :original_sample_size]

            if not return_dict:
                  return (audio,)

            return AudioPipelineOutput(audios=UpperCAmelCase	)



"""simple docstring"""



from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase_							=							logging.get_logger(__name__)

lowercase_							=							{
    's-JoL/Open-Llama-V1': 'https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json',
}







class    __a						( __snake_case							):
				lowerCamelCase     :       Union[str, Any]			       ='open-llama'







				def __init__( self      ,	UpperCAmelCase=10_0000      ,	UpperCAmelCase=4096      ,	UpperCAmelCase=1_1008      ,	UpperCAmelCase=32      ,	UpperCAmelCase=32      ,	UpperCAmelCase="silu"      ,	UpperCAmelCase=2048      ,	UpperCAmelCase=0.0_2      ,	UpperCAmelCase=1E-6      ,	UpperCAmelCase=True      ,	UpperCAmelCase=0      ,	UpperCAmelCase=1      ,	UpperCAmelCase=2      ,	UpperCAmelCase=False      ,	UpperCAmelCase=True      ,	UpperCAmelCase=0.1      ,	UpperCAmelCase=0.1      ,	UpperCAmelCase=True      ,	UpperCAmelCase=True      ,	UpperCAmelCase=None      ,	**UpperCAmelCase      ,	):

					'''simple docstring'''






					lowerCAmelCase_      							=			vocab_size
					lowerCAmelCase_      							=			max_position_embeddings
					lowerCAmelCase_      							=			hidden_size
					lowerCAmelCase_      							=			intermediate_size
					lowerCAmelCase_      							=			num_hidden_layers
					lowerCAmelCase_      							=			num_attention_heads
					lowerCAmelCase_      							=			hidden_act
					lowerCAmelCase_      							=			initializer_range
					lowerCAmelCase_      							=			rms_norm_eps
					lowerCAmelCase_      							=			use_cache
					lowerCAmelCase_      							=			kwargs.pop(
					    '''use_memorry_efficient_attention'''      ,	UpperCAmelCase   )
					lowerCAmelCase_      							=			hidden_dropout_prob
					lowerCAmelCase_      							=			attention_dropout_prob
					lowerCAmelCase_      							=			use_stable_embedding
					lowerCAmelCase_      							=			shared_input_output_embedding
					lowerCAmelCase_      							=			rope_scaling
					self._rope_scaling_validation()

					super().__init__(
					    pad_token_id=UpperCAmelCase      ,	bos_token_id=UpperCAmelCase      ,	eos_token_id=UpperCAmelCase      ,	tie_word_embeddings=UpperCAmelCase      ,	**UpperCAmelCase      ,	)







				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					if self.rope_scaling is None:
						return

					if not isinstance(self.rope_scaling      ,	UpperCAmelCase   ) or len(self.rope_scaling   ) != 2:
						raise ValueError(
						    '''`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, '''
						    F"""got {self.rope_scaling}"""   )
					lowerCAmelCase_      							=			self.rope_scaling.get('''type'''      ,	UpperCAmelCase   )
					lowerCAmelCase_      							=			self.rope_scaling.get('''factor'''      ,	UpperCAmelCase   )
					if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
						raise ValueError(
						    F"""`rope_scaling`'s name field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"""   )
					if rope_scaling_factor is None or not isinstance(UpperCAmelCase      ,	UpperCAmelCase   ) or rope_scaling_factor <= 1.0:
						raise ValueError(F"""`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}"""   )